Latest Research News on Markov Model: Oct – 2019

Hidden Markov model decomposition of speech and noise

The problem of automatic speech recognition within the presence of meddling signals and noise with applied mathematics characteristics starting from stationary to quick dynamical and impulsive is mentioned. a way of signal decomposition victimisation hidden Andrei Markov models is delineated . this is often a generalization of typical hidden Andrei Markov modeling that has associate optimum technique of rotten synchronic processes. The technique exploits the flexibility of hidden Andrei Markov models to model dynamically variable signals so as to accommodate synchronous  processes, together with meddling signals as complicated as speech. this type of signal decomposition has wide implications for signal separation generally and improved speech modeling above all. the appliance of decomposition to the matter of recognition of speech contaminated with noise is stressed. [1]

The Hierarchical Hidden Markov Model: Analysis and Applications

We introduce, analyze and demonstrate a algorithmic stratified generalization of the wide used hidden mathematician models, that we have a tendency to name stratified Hidden mathematician Models (HHMM). Our model is impelled by the complicated multi-scale structure that seems in several natural sequences, significantly in language, handwriting and speech. we have a tendency to ask for a scientific unsupervised  approach to the modeling of such structures. By extending the quality Baum-Welch (forward-backward) algorithmic rule, we have a tendency to derive associate degree economical procedure for estimating the model parameters from unlabelled knowledge. we have a tendency to then use the trained model for automatic stratified parsing of observation sequences. [2]

A Markov Model of Switching-Regime ARCH

In this article I gift a brand new approach to model additional realistically the variability of economic statistic. I develop a Markov-ARCH model that comes with the options of each Hamilton’s switching-regime model and Engle’s autoregressive conditional heteroscedasticity (ARCH) model to look at the problem of volatility persistence within the monthly excess returns of the three-month Treasuries. the problem may be resolved by taking under consideration occasional shifts within the straight line variance of the Markov-ARCH method that cause the spurious persistence of the volatility process. I determine 2 periods throughout that there’s a regime shift, the 1974:2–1974:8 amount related to the oil shock and also the 1979:9–1982:8 period associated with the Federal Reserve’s change. The variance approached straight lineally in these 2 episodes is over ten times as high because the asymptotic variance for the rest of the sample. [3]

Markov model and markers of small cell lung cancer: assessing the influence of reversible serum NSE, CYFRA 21-1 and TPS levels on prognosis

High humour NSE and advanced growth stage area unit well-known negative prognostic determinants of little cell carcinoma (SCLC) once ascertained at presentation. However, such variables area unit reversible unwellness indicators as they’ll modification throughout the course of medical aid. the connection between risk of death and marker level and unwellness state throughout treatment of SCLC therapy isn’t acknowledged. a complete of fifty two patients with SCLC were followed throughout cisplatin-based therapy (the median range of growth standing and marker level assessments was 4). The time-homogeneous Markoff model was employed in order to analyse on an individual basis the prognostic significance of modification within the state of the humour marker level (NSE, CYFRA 21-1, TPS) or the modification in growth standing. [4]

Missclassification of HIV Disease Stages with Continuous Time Hidden Markov Models

The purpose of this study is to explore the easy mathematician and Hidden Markov models with continuous time to analyze unwellness progression of HIV/AIDS patients underneath ART follow-up at Shashemene Referral Hospital, Ethiopia. The msm R package is employed for the analysis. Results from the easy mathematician model reveals that the unwellness progression of the HIV/AIDS patients thought of tend to maneuver towards the healthier than the more severe state. The mean waiting time for the healthiest state is considerably more than the opposite transient states. the overall length of your time keep during a state declines with severity of the unwellness stages. Analysis of the misclassification model provides transition rates of actuality states. Estimation of the transition rates of actuality states are found to be comparatively smaller compared to those obtained by the easy mathematician model. [5]

Reference

[1] Varga, A. and Moore, R.K., 1990, April. Hidden Markov model decomposition of speech and noise. In International Conference on Acoustics, Speech, and Signal Processing (Web Link)

[2] Fine, S., Singer, Y. and Tishby, N., 1998. The hierarchical hidden Markov model: Analysis and applications. Machine learning, 32(1), (Web Link)

[3] Cai, J., 1994. A Markov model of switching-regime ARCH. Journal of Business & Economic Statistics, 12(3), (Web Link)

[4] Markov model and markers of small cell lung cancer: assessing the influence of reversible serum NSE, CYFRA 21-1 and TPS levels on prognosis
J-M Boher, J-L Pujol, J Grenier & J-P Daurès
British Journal of Cancer volume 79, (Web Link)

[5] Habtemichael, T., Goshu, A. and Buta, G. (2018) “Missclassification of HIV Disease Stages with Continuous Time Hidden Markov Models”, Journal of Advances in Medicine and Medical Research, 25(11), (Web Link)

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